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Learn to call variants from whole genome sequencing data using GATK best practice workflow in less than an hour with Bioinformagician. Includes detailed tutorials and code.
Learn to analyze VCF files in bioinformatics, understand genotypes, calculate alternate allele frequency, and distinguish between VCF and gVCF files in under an hour with Bioinformagician.
Learn to filter and annotate genetic variants using GATK's Funcotator tool in this detailed tutorial by Bioinformagician. Less than 1-hour workload.
Heatmaps This course covers the workflow steps for WGCNA, teaching how to get and manipulate data, detect outliers, normalize, filter and identify modules, and visualize results.
. This syllabus covers an intro to trajectory analysis and pseudotime; when, which, and how methods are used; and workflow steps to create a cell_data_set, process, analyze and visualize data.
Learn to process RNA-Seq reads and generate counts matrix in less than an hour with Bioinformagician. The tutorial includes quality control, trimming, alignment, and quantification steps.
Explore bioinformatics and computational biology with an expert from Bioinformagician, discussing research fields, career paths, job prospects, and graduate work preparation in a 1-2 hour session.
Bioinformagician's tutorial offers a detailed walk-through of pseudo-bulk differential expression analysis for single-cell RNA-Seq data in R, in under an hour.
Learn to identify markers in single-cell RNA-Seq using Seurat in R with Bioinformagician's tutorial. Understand study design, data loading, visualization, and differential expression.
Learn to merge and correct batch effect in single-cell RNA sequencing datasets using Seurat in R with Bioinformagician's detailed tutorial in under an hour.
Learn to analyze RNA-seq count data and detect differentially expressed genes using DESeq2 in R with Bioinformagician. Less than 1-hour workload.
Learn to analyze single-cell RNA sequencing data using the Seurat package in R with Bioinformagician's detailed tutorial. Less than 1-hour workload.
Learn to visualize gene expression data using ggplot2 in R with Bioinformagician. This hands-on tutorial covers barplot, density plot, boxplot, scatterplot, and heatmap creation.
Learn to manipulate gene expression data using R and dplyr in this hands-on tutorial by Bioinformagician. Ideal for beginners, it covers data reading, metadata retrieval, and basic data manipulation.
Learn to annotate cell types in single-cell RNA-Seq data using SingleR with Bioinformagician. This under 1-hour tutorial covers workflows, strategies, diagnostics, and visualization.
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